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, including genomic, transcriptomic, epigenomic, proteomic, metabolomic, and circulating biomarker data derived from patient tissues and liquid biopsies. Our goal is to characterize the molecular processes
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including transcriptomics, proteomics, metabolomics (including stable isotope based in vitro and in vivo metabolomics) and mass spectrometry-based profiling. Of note, the overall chargeback revenue target
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implement the computational algorithms, automate data analyses workflow, curate published pan-cancer datasets, and build prediction models for novel transcriptome features analyses identified using bulk and
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projects addressing the molecular understanding of breast cancer through the analysis of single-cell genomics, spatial genomics, transcriptomics and imaging datasets. Following directions from
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of genomic, epigenomic, or transcriptomic data. Ability to integrate data generated using different technologies and experimental platforms. Ability to create clear data visualisations and publication-quality
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of bioinformatics and statistical analysis of large-scale genomic datasets. Familiar with next-generation sequencing technologies and data analysis. Analyzing single-cell transcriptomic and epitranscriptomic datasets
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, antimicrobial resistance, bacteriophage biology, host–pathogen interactions, and One Health infectious diseases. This position offers an opportunity to work at the interface of microbial genomics, transcriptomics
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approaches combining live-cell imaging, single-cell transcriptomics (fate-seq), and mechanistic mathematical modeling to investigate the non-genetic origins of resistance to anticancer therapies. The PhD
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cancer models Experience with single-cell/single-nucleus genomics and spatial transcriptomics Computational analysis skills in R and/or Python A record of first-author publication To apply: Please send a
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H. Lee Moffitt Cancer Center & Research Institute | Tampa, Florida | United States | about 2 months ago
microenvironment, and further reprogram the T cells to such states. We use patient-derived samples, genetically engineered mouse models, along with state of the art molecular methodologies, including transcriptomics